Faster, lower-cost measures of multiphase permeability of conventional reservoirs are promised by a digital rock analysis method developed by BP and Exa, which is marketing software to measure relative permeability. This paper describes the development of “digital-rocks” technology, in which high-resolution 3D image data are used in conjunction with advanced modeling and simulation methods to measure petrophysical rock properties.
Digital core generated from micro CT images of rock sample cutting and results obtained from digital core analysis are presented in this work as a substitute of conventional core study for Petrophysical evaluation. Conventional core extraction during drilling, core preservation and analysis are expensive, time consuming processes and often unavailable for small size fields. Moreover, routine and special core analysis results are a critical input for petrophysical characterization. In this situation, digital core study appears to be a cost effective substitute to ensure and validate petrophysical evaluation results.
High resolution 3D micro CT imaging and analysis was done on rock samples cut during drilling or on sidewall core plugs cut by wireline logging tool. Segmented micro CT image slices when combined in 3D space in three orthogonal directions, can be termed as digital core. Solid rock matrix, clay filled and porous rock portions are distinctly separable using micro CT images and their volume fractions can be estimated. Detail textural analysis in terms of Grain and pore throat size distribution of the rock is possible from digital core which controls storage capacity and flow behavior. Two critical petrophysical input parameters for fluid saturation (Sw) estimation are cementation exponent (m) and saturation exponent (n). These parameters are commonly computed from special core analysis (SCAL) on conventional core plugs. But digital core study can provide the estimates of ‘m’ and ‘n’ which replace the need of SCAL.
Digital core study has been carried out in three different reservoirs in west and east coast of India and the results were analyzed. Porosity and permeability data obtained from digital core was first compared with log analysis results and then used to identify different petro physical rock types (PRT). Fluid saturation (Sw) was estimated from resistivity log by using ‘m’ and ‘n’ exponent obtained from digital core seems to be more realistic and corroborates with well test results. Porosity, permeability, water saturation and rock types (PRT) were helped to build geo-cellular model (GCM) for small and marginal reservoir.
Enhanced reservoir characterization by using digital core study result has helped in better understanding and decision making for small and marginal fields where limited well data is available. Finally this leads to the preparation of field development plan (FDP). Digital core technique is less expensive, having quick turnaround time than conventional coring which has translated into high value business impact for any development project.
Is the Cloud Mature Enough for High-Performance Computing? The majority of Shell’s HPC work helps support its seismic imaging operations. The company supports 45 HPC applications, with the bulk of its processing and production workload taking place in-house. Oil and gas is in the midst of a pervasive digital transformation in which the industry is changing the way it manages assets, the way it interacts with customers, and the way it develops internal workflows. Perhaps one of the most significant impacts of this transformation, however, is the way in which companies characterize their subsurface data.
Rock mechanical properties is essential for several geomechanical applications such as wellbore stability analysis, hydraulic fracturing design, and sand production management. These are often reliably determined from laboratory tests by using cores extracted from wells under simulated reservoir conditions. Unfortunately, most wells have limited core data. On the other hand, wells typically have log data, which can be used to extend the knowledge of core-based mechanical properties to the entire field. Core to log integration of rock mechanical properties and its interpretation is limited by our current understanding of rock physics. The gap is clearly evident where approximations such as empirical relationship between dynamic and static mechanical properties are used for rock failure estimation. This paper presents a hybrid framework that combines advances in digital rock physics (DRP) and machine learning (ML) to predict rock mechanical propertiy (e.g., Young's modulus) from rock mineralogy and texture to improve the accuracy of mechanical properties determined from log data.
In this study, mineralogy, density, and porosity data are obtained from routine core analysis and rock mechanical property from triaxial compression tests. In our methodology, we utilized DRP models which were calibrated against core data and then generate rock mechanical property, for intervals for which triaxial measurements were not available. Mineralogy and texture data are used to create DRP models by numerically simulating rock-forming geological process including sedimentation, compaction, and cementation. Rock mechanical properties derived from DRP are used to enhance the set of training data for the ML algorithm to establish a correlation between rock mineralogy, texture, and mechanical property and construct the ML-based rock mechanical property model. The ML model generates Young's modulus predictions and are compared with the laboratory measurements.
The predicted Young's modulus of rock models from the combined approach has a good agreement with the laboratory measurements. Two quantitative measures for estimation accuracy are calculated and examined including the correlation coefficient and the mean absolute percentage error. Cross-correlation plots between the Young's modulus predicted from the ML model and experimental results show high correlation coefficients and small error. The results of the study show that DRP model can be used to feed the ML model with reliable data so that the prediction accuracy can be improved. The results of this work will provide an avenue of learning from the formation lithology and using the knowledge to predict rock mechanical properties.
Digital rocks obtained from high-resolution micro-computed tomography (micro-CT) imaging has quickly emerged as a powerful tool for studying pore-scale transport phenomena in petroleum engineering. In such frameworks, digital rock analysis usually carries the problematic aspect of segmenting greyscale images into different phases for quantifying many physical properties. Fine pore structures, such as small rock fissures, are usually lost during segmentation. In addition, user bias in this process can lead to significantly different results. An alternative approach based on deep learning is proposed. Convolutional Neural Networks (CNN) are utilized to rapidly predict several porous media properties from 2D greyscale micro-computed tomography images in a supervised learning frame. A dataset of greyscale micro-CT images of three different sandstones species is prepared for this study. The image dataset is segmented, and pore networks are extracted to compute porosity, coordination number, and average pore size for training and validating our model predictions. The greyscale images (input) and the computed properties (output) are uploaded to a deep neural network for training and validation in an end-to-end regression scheme. Overall, our model estimates porosity, coordination number, and average pore size with an average error of 0.05, 0.17, and 1.8μm, respectively. Training wall-time and prediction error analysis are also discussed. This is a first step to use artificial intelligence and machine learning methods for the robust prediction of porous media properties from unprocessed image-driven data.
Jin, Xu (PetroChina Research Institute of Petroleum Exploration & Development) | Yu, Chen (PetroChina Research Institute of Petroleum Exploration & Development) | Wang, Xiaoqi (PetroChina Research Institute of Petroleum Exploration & Development) | Liu, Xiaodan (PetroChina Research Institute of Petroleum Exploration & Development) | Li, Jianming (PetroChina Research Institute of Petroleum Exploration & Development) | Jiao, Hang (PetroChina Research Institute of Petroleum Exploration & Development) | Su, Ling (PetroChina Research Institute of Petroleum Exploration & Development)
High heterogeneity and small pore throat characterize the mineral compositions of complex reservoirs. The evaluation of the effectiveness of reservoir space, the detailed evaluation of the rock structure, and the evaluation of fluid occurrence and migration all determine the possibility of oil and gas exploration within complex reservoirs. The technologies and workflows used for the multi-scale digital rock evaluation of complex reservoirs analyze the reservoir space (i.e., the pore throat and fractures), the rock structure (i.e., mineral and organic matter), and fluid characteristics. For the reservoir space, two-dimensional large-area analysis is sufficient to evaluate heterogeneity and multi-scale selection across 6–7 orders of magnitude. Three-dimensional space distributions of pore throat and fractures are precisely depicted through a combination of multi-scale CT and FIB-SEM. The developing agent facilitates the analysis of the micro-pore connectivity effectively. For the solid components, a quantitative evaluation of mineral composition and distribution is possible using Qemscan, and organic matter morphology and distribution are quantitatively assessed using the three-dimensional FIB-SEM. To characterize the fluid, charging effects may aid in the identification and characterization of residual, organic fluid traces. We perform the physical modeling of shale oil occurrence and migration by using nano materials that have adjustable pore sizes, adjustable wettability, and adjustable surface microstructure to understand the impact that each factor has on shale oil occurrence and the lowest pore sizes in which fluid can migrate. We use molecular simulations to observe oil and gas aggregation mechanisms and the diffusion potential in inorganic and organic nano pores. To supplement the conventional reservoir analysis, multi-scale digital rock evaluation, and its specific applications, provide technical proof for effective complex reservoir assessment, including shale oil and gas reservoirs, tight sandstone oil and gas reservoirs, and deep oil and gas reservoirs, as well as a quantitative oil-gas probability evaluation.
Summary We developed a numerical framework and procedures to generate three-dimensional grain packs of spherical and nonspherical (regular and irregular) grains with prescribed size distributions. An efficient approach to create multiple realizations of nonspherical irregularly shaped grains using coherent noise modification of the spherical grains surface is introduced. Various three-dimensional random loose and dense grain packs show the validity, flexibility, and consistency of the simulator compared to previous studies. Introduction Studies on granular media are essential for understanding a wide range of physical phenomena including fluid flow, stress and strain, heat conduction, and electrical effects. Commonly in geoscience, standard rock samples from specific formations are used for scientific studies (e.g., Berea sandstone, Baker dolomite, and Indiana limestone).
The use of digital rock physics (DRP) in obtaining realistic measurements comparable to conventional laboratorymeasurements depends on many factors. One key factor is our ability to simulate rigorous physical processes at thepore scale. In this work, we perform numerical modeling to capture the physics related to coupled fluid-solidinteraction (FSI) and the frequency-dependence of porescale fluid flow in response to pore pressure heterogeneitiesat the pore scale. First, we perform the numerical simulations on a simple 2D geometry consisting of a pair ofconnected cracks to establish the numerical method. We compute and contrast the stresses and pore pressures fromour numerical method with the commonly used method that considers only structural mechanics, ignoring FSI. We findthe stresses and pore pressures of these two cases are similar for low frequencies (10 Hz). However, at higherfrequencies we observed pore pressure heterogeneities from the FSI numerical method that cannot be modeled using thestructural mechanics approach. The phase difference between the applied harmonic displacement and the pore fluid pressure starts increasing at high frequencies (100KHz) and then decreases as we keep increasing the frequencies, until we reach very high frequencies (10 MHz) at which scattering effects become predominant. We applied our numerical method on a 3D digital rock sample of Berea sandstone for frequencies 10 Hz and 10 MHz. We observe that the pore pressure at the low frequency is homogeneous and the fluid is in a relaxed state, whereas at high frequency, the pore pressure is heterogeneous, and the fluid is in an unrelaxed state. This type of numericalmethod helps in understanding the dynamic effects of fluid at different frequencies that results in velocity dispersion and attenuation. This method can be help bridge the gap between seismic measurements taken at differentfrequencies and will contribute towards better understanding of pore-scale rock physics.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 202A (Anaheim Convention Center)
Presentation Type: Oral
We explore the possibility to use digital rocks to determine poroelastic parameters which are difficult to extract from well-log or laboratory measurements. The Biot coefficient and the drained pore modulus are important in the compaction problem. The pore modulus represents the ratio of pore volume change to confining pressure when the fluid pressure is constant. In laboratory experiments, bulk volume changes are accurately measured by sensors attached to the outer surface of the rock sample. In contrast, pore volume changes are notoriously difficult to measure because these changes need to quantify the pore boundary deformation. Hence, accurate measures of the drained pore modulus are challenging. We simulate static deformation experiments at the pore-scale utilizing digital rock images. We model an Ottawa F-42 sand pack obtained from X-ray micro-tomographic images. We calculate the change in pore volume using a new post-processing algorithm, which allows us to compute the local changes in pore volume due to the applied load. This process yields an accurate drained pore modulus. We then use an alternative estimate of the drained pore modulus. We exploit its relation to the drained bulk modulus and the solid phase bulk modulus (i.e., Biots coefficient) using the digital rock workflow. Finally, we compare the drained pore modulus values obtained from these two independent analyses and find reasonable agreement.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 202A (Anaheim Convention Center)
Presentation Type: Oral
We propose a quasi-static finite difference method for calculating the effective elastic moduli of digital rock models. The effective elastic moduli of a model are derived from a system of equations that are established based on Hooke’s law, which relates the prescribed stresses at model boundaries and the average strains calculated from modeling. The static stress-strain relationship is obtained by solving a damped wave equation using the standard staggered-grid finite difference scheme. The accuracy of this proposed method is validated against the Backus-average for a laminated model and published results for a porous digital rock model.
Presentation Date: Tuesday, October 16, 2018
Start Time: 9:20:00 AM
Location: Poster Station 19
Presentation Type: Poster